> ## Documentation Index
> Fetch the complete documentation index at: https://docs.dappier.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Activepieces

[**Activepieces**](https://www.activepieces.com) is an open-source, no-code automation platform that enables users to connect apps, automate workflows, and integrate AI-powered actions through a visual interface. It supports scheduled triggers, conditional logic, and integrations with tools like Gmail, OpenAI, Slack, and Twitter—allowing technical and non-technical users to build scalable automations with ease.

[**Dappier**](https://dappier.com/developers/) is a platform that connects LLMs and automation frameworks to real-time, rights-cleared data from trusted sources, including web search, finance, and news. By providing enriched, prompt-ready data, Dappier empowers automations with verified and up-to-date information for a wide range of applications.

## Real-Time Use Cases

The following real-time workflows demonstrate how to use Dappier actions inside Activepieces to build dynamic, real-world automations. Each example highlights scheduled triggers, real-time data retrieval, AI-powered text generation, and multi-app integrations such as Twitter and Gmail.

* [**Stock Market Analyst Bot**](https://docs.dappier.com/cookbook/recipes/activepieces-stock-market-workflow)
  Automatically analyzes any stock or public company mentioned in your inbox. This workflow identifies the stock ticker, pulls live financial data using Dappier, generates a full investment report using OpenAI, and sends it back via Gmail.

* [**Auto-Tweet Real-Time News**](https://docs.dappier.com/cookbook/recipes/activepieces-auto-tweet-real-time-news-workflow)
  Tweets trending AI-related content every few minutes. It uses Dappier to pull live headlines, OpenAI to write engaging tweets under 280 characters, and Twitter to publish them—completely hands-free.

## Overview

The Activepieces integration with [Dappier](https://dappier.com/) enables users to build powerful, real-time automations by connecting their flows to rights-cleared data from the public web, financial markets, and media publishers.

By combining Dappier’s live data tools with OpenAI and other pieces in Activepieces, users can build end-to-end workflows that automatically respond to the latest trends, financial insights, or curated content—without writing any backend logic.

This integration includes access to the following Dappier-powered actions:

* **Real Time Data** – Perform live Google-like web searches including the latest news, weather, deals, events, and general updates.
* **Stock Market Data** – Retrieve real-time financial data, earnings updates, stock prices, and market sentiment.
* **Sports News** – Access sports headlines and breaking stories from publishers like Sportsnaut, LAFB Network, Ringside Intel, and more.
* **Lifestyle News** – Get curated stories from lifestyle sites like The Mix, Nerdable, Snipdaily, and Familyproof.

Each action accepts natural language queries and can be added to your to create responsive, AI-driven automations.

## Usage with Activepieces

To use Dappier actions inside your Activepieces workflow, you’ll start by setting up a new flow and then adding the Dappier piece using the visual editor. Dappier actions can be placed after any trigger (like a schedule or email) and paired with OpenAI or other integrations to act on live data.

### Step 1: Create a New Flow

Go to [cloud.activepieces.com](https://cloud.activepieces.com) and click **"New Flow"**. Give your flow a name and click **"Start building"**.

### Step 2: Add a Trigger

Choose a trigger based on your use case, such as:

* **Schedule** – Run the flow every X minutes
* **Gmail** – Trigger on new incoming emails
* **Slack** – Trigger on a command or message
* **Webhook** – Trigger the flow from an external system

### Step 3: Add the Dappier Action

Click the **"+"** button to add a new step, then search for and select the **Dappier** piece.

Choose one of the following supported actions:

* `Real Time Data`
* `Stock Market Data`
* `Sports News`
* `Lifestyle News`

You’ll be prompted to authenticate with your Dappier API key, and then enter a natural language **query** to retrieve content dynamically.

### Step 4: Chain Additional Steps (Optional)

Add downstream actions like:

* **OpenAI** – Summarize or rephrase the results into human-friendly content
* **Gmail** – Send the result as an email
* **Twitter** – Post content as an auto-generated tweet
* **Slack** – Send alerts or summaries to a channel

Once done, click **"Publish"** to activate the flow.

## Available Actions

After adding the Dappier piece to your flow, you can select from four real-time data actions. Each action supports natural language queries and returns structured results that can be used directly or passed to other pieces like OpenAI, Gmail, or Twitter.

***

### Real Time Data

Perform live web searches to access the latest information from trusted sources including Google-indexed news, weather updates, travel alerts, shopping deals, and more.
**Use case**: General-purpose content automation triggered by live web search results.

#### Parameters

* #### `query` (str)

  A natural language description of what you're searching for.
  **Required**
  **Example**:

  ```text theme={null}
  Latest news on artificial intelligence and machine learning this week
  ```

#### Output

Returns a real-time, formatted response with summaries of recent search results relevant to the query.

***

### Stock Market Data

Access real-time stock data and financial news powered by Polygon.io. Use it to fetch company earnings, valuation metrics, price changes, and live market performance.
**Use case**: Investment report generation, stock alert bots, and financial monitoring.

#### Parameters

* #### `query` (str)

  A stock ticker or financial question written in natural language.
  **Required**
  **Example**:

  ```text theme={null}
  Show stock performance and financial updates for AAPL
  ```

#### Output

Returns up-to-the-minute stock information including price movements, earnings highlights, and sentiment-tagged headlines.

***

### Sports News

Retrieve real-time sports stories from top-tier media publishers like Sportsnaut, Ringside Intel, LAFB Network, Minnesota Sports Fan, and Bounding Into Sports.
**Use case**: Sports digest generators, highlight summaries, and AI-driven fan updates.

#### Parameters

* ##### `query` (str)

  A natural language request for sports-related content.
  **Required**
  **Example**:

  ```text theme={null}
  Latest sports news
  ```

* ##### `number_of_results` (int)

  Maximum number of articles to retrieve.

* ##### `preferred_domain` (str)

  Domain to prioritize in the results (e.g., `sportsnaut.com`).

* ##### `num_articles_from_domain` (int)

  Minimum number of articles required from the preferred domain.

* ##### `search_algorithm` (str)

  Strategy for result retrieval: `"most_recent"`, `"semantic"`, `"most_recent_semantic"`, or `"trending"`.

#### Output

Returns a formatted list of sports headlines with summaries and source links.

***

### Lifestyle News

Get curated lifestyle updates from sources like The Mix, Nerdable, Snipdaily, and Familyproof. Covers entertainment, wellness, pop culture, and human-interest stories.
**Use case**: Lifestyle newsletters, trending content generators, or social media content pipelines.

#### Parameters

* ##### `query` (str)

  A natural language request for lifestyle-related content.
  **Required**
  **Example**:

  ```text theme={null}
  Latest lifestyle news
  ```

* ##### `number_of_results` (int)

  Maximum number of articles to retrieve.

* ##### `preferred_domain` (str)

  Domain to prioritize in the results (e.g., `reuters.com`).

* ##### `num_articles_from_domain` (int)

  Minimum number of articles required from the preferred domain.

* ##### `search_algorithm` (str)

  Strategy for result retrieval: `"most_recent"`, `"semantic"`, `"most_recent_semantic"`, or `"trending"`.

#### Output

Returns a list of lifestyle-focused articles with context-rich summaries and links.

## Conclusion

Integrating Dappier with Activepieces unlocks powerful, real-time automation capabilities that require no code. Whether you're building a financial analyst bot, an AI news tweet generator, or a personalized content delivery system, Dappier provides high-quality, rights-cleared data from trusted sources—ready for use inside any Activepieces flow.

With just a few steps, you can:

* Trigger automations on a schedule or based on external events
* Retrieve live web or financial data using Dappier
* Use OpenAI to convert raw data into human-friendly summaries
* Deliver content through Gmail, Twitter, Slack, or custom channels

By combining Dappier’s live data tools with Activepieces' visual automation builder, you can create responsive, intelligent workflows that stay up-to-date with the world—no backend or infrastructure required.

For real-world recipes, see the full cookbooks at:

* [Stock Market Analyst Bot](https://docs.dappier.com/cookbook/recipes/activepieces-stock-market-workflow)
* [Auto-Tweet Real-Time News](https://docs.dappier.com/cookbook/recipes/activepieces-auto-tweet-real-time-news-workflow)